Medical Decision

Medical decision-making is increasingly leveraging artificial intelligence to improve diagnostic accuracy, treatment planning, and risk stratification. Current research focuses on developing and validating sophisticated models, including graph neural networks and deep reinforcement learning algorithms, capable of processing diverse and complex patient data (e.g., demographics, vital signs, medical images). A critical challenge lies in ensuring the transparency and explainability of these models to build trust and facilitate clinical adoption, necessitating the development of robust explainable AI (XAI) methods that align with clinical expertise. Ultimately, these advancements aim to enhance the quality and efficiency of healthcare by providing clinicians with more accurate, personalized, and interpretable decision support tools.